Modeling EEG and Eye-tracking in Network Architectures:  Sequential modeling through recurrent neural networks like LSTM has been a popular technique for extracting relevant features from the EEG [1]}, [2]} and eye-tracking data [3]}, [4]}. However, these models overlook the functional connectivities between the the EEG electrodes and their effect on the gaze patterns, leading to spatio-temporal information loss for sequence classification tasks [5]}. We overcome this challenge by learning the interdependency between the EEG electrodes as part of the training process. Recently, many graph based methods have been proposed for extracting spatio-temporal information from EEG signals [6]}, [7]}, [8]}, [9]}. However, most of these methods treat the graph network connections as static and do not learn the functional connectivities between the EEG electrodes as a part of the training process. We hypothesize that learning the graph structure in an online fashion helps the model exploit the latent spatial interdependencies in the brain signals without relying on external biases about brain graph modeling.
